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Atomic-Germ/
Qwen3.8-Distilled-1.2B-NPU2

Name: Qwen3.8-Distilled-1.2B-NPU2
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IF YOU USE COMMUNITY QWEN MODELS DO NOT UPGRADE TO FLM v1.0.2+

Qwen3.8-Distilled-1.2B-NPU2

FastFlowLM Q4NX conversion of FastFlowLM/LFM2.5-1.2B-Thinking-NPU2 for AMD XDNA NPU inference.

This repository contains a quantized Q4NX port of the model, compiled for the FastFlowLM (FLM) runtime. It is not a GGUF file.

Item	Value
Source model	`FastFlowLM/LFM2.5-1.2B-Thinking-NPU2`
Source GGUF	`Qwen3.8_1.2B_LFM_Distillation-q4_0.gguf`
Weights	`model.q4nx` (953.76 MB)
Modality	language
FLM version	`1.0.1`
Converted	2026-08-14

Source repository

Metadata from the upstream Hugging Face repository:

Item	Value
License	other
Base model	`LiquidAI/LFM2.5-1.2B-Base`
Library	`transformers`
Model type	`lfm2`
Pipeline	`text-generation`
Repo revision	`2c6b0a07d5c05b9996a5588ae207a68ded6fa25c`

Usage

Install and run

This repository works with flm-add, a small installer that copies the model into the FastFlowLM user directory and registers the tag. It never modifies the system FastFlowLM install.

pip install flm-add or uv tool install flm-add

bash

uv tool install flm-add
flm-add Atomic-Germ/Qwen3.8-Distilled-1.2B-NPU2 --tag qwen3.8-distilled:1.2b --family lfm2.5-tk
FLM_CONFIG_PATH="$HOME/.config/flm/model_list.json" FLM_XCLBIN_PATH="$HOME/.config/flm" flm run qwen3.8-distilled:1.2b

Files

File	Description
`model.q4nx`	Quantized weights (Q8_0 / Q4_1 / BF16)
`config.json`	FLM runtime configuration
`tokenizer.json`	Tokenizer vocabulary
`tokenizer_config.json`	Tokenizer configuration
`chat_template.jinja`	Chat template

FLM Bench

Tested on an AMD Ryzen AI 340 Framework 13 laptop.

Context Length	TTFT (s) (mean ± std)	Prefill Speed (tok/s) (mean ± std)	Decoding Speed (tok/s) (mean ± std)
1k	0.869 ± 0.017	1127.32 ± 22.53	40.37 ± 0.34
2k	1.341 ± 0.007	1454.35 ± 7.12	41.56 ± 0.93
4k	2.313 ± 0.042	1681.14 ± 30.38	39.85 ± 0.83
8k	4.312 ± 0.082	1801.56 ± 34.15	37.38 ± 0.50
16k	9.130 ± 0.014	1699.75 ± 2.46	34.06 ± 0.47
32k	21.353 ± 0.002	1453.12 ± 0.39	27.07 ± 1.05

Source model card

Try LFM • Documentation • LEAP

LFM2.5-1.2B-Thinking

LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.

Best-in-class performance: A 1.2B model rivaling much larger models, bringing high-quality AI to your pocket.
Fast edge inference: 239 tok/s decode on AMD CPU, 82 tok/s on mobile NPU. Runs under 1GB of memory with day-one support for llama.cpp, MLX, and vLLM.
Scaled training: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning.

Find more information about LFM2.5 in our blog post.

🗒️ Model Details

Model	Parameters	Description
LFM2.5-1.2B-Base	1.2B	Pre-trained base model for fine-tuning
LFM2.5-1.2B-Instruct	1.2B	General-purpose instruction-tuned model
LFM2.5-1.2B-Thinking	1.2B	General-purpose reasoning model
LFM2.5-1.2B-JP	1.2B	Japanese-optimized chat model
LFM2.5-VL-1.6B	1.6B	Vision-language model with fast inference
LFM2.5-Audio-1.5B	1.5B	Audio-language model for speech and text I/O

LFM2.5-1.2B-Thinking is a general-purpose text-only model with the following features:

Number of parameters: 1.17B
Number of layers: 16 (10 double-gated LIV convolution blocks + 6 GQA blocks)
Training budget: 28T tokens
Context length: 32,768 tokens
Vocabulary size: 65,536
Languages: English, Arabic, Chinese, French, German, Japanese, Korean, Spanish
Generation parameters:
- temperature: 0.1
- top_k: 50
- top_p: 0.1
- repetition_penalty: 1.05

Model	Description
LFM2.5-1.2B-Thinking	Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM.
LFM2.5-1.2B-Thinking-GGUF	Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage.
LFM2.5-1.2B-Thinking-ONNX	ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile).
LFM2.5-1.2B-Thinking-MLX	MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework.

We recommend using it for agentic tasks, data extraction, and RAG. It is not recommended for knowledge-intensive tasks and programming.

Chat Template

LFM2.5 uses a ChatML-like format. See the Chat Template documentation for details. Example:

<|startoftext|><|im_start|>system
You are a helpful assistant trained by Liquid AI.<|im_end|>
<|im_start|>user
What is C. elegans?<|im_end|>
<|im_start|>assistant

You can use tokenizer.apply_chat_template() to format your messages automatically.

Tool Use

LFM2.5 supports function calling as follows:

Function definition: We recommend providing the list of tools as a JSON object in the system prompt. You can also use the tokenizer.apply_chat_template() function with tools.
Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between <|tool_call_start|> and <|tool_call_end|> special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
Function execution: The function call is executed, and the result is returned as a "tool" role.
Final answer: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.

See the Tool Use documentation for the full guide. Example:

<|startoftext|><|im_start|>system
List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|im_end|>
<|im_start|>user
What is the current status of candidate ID 12345?<|im_end|>
<|im_start|>assistant
<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
<|im_start|>tool
[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|>
<|im_start|>assistant
The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>

🏃 Inference

LFM2.5 is supported by many inference frameworks. See the Inference documentation for the full list.

Name	Description	Docs	Notebook
Transformers	Simple inference with direct access to model internals.	Link
vLLM	High-throughput production deployments with GPU.	Link
llama.cpp	Cross-platform inference with CPU offloading.	Link
MLX	Apple's machine learning framework optimized for Apple Silicon.	Link	—
LM Studio	Desktop application for running LLMs locally.	Link	—

Here's a quick start example with Transformers:

python

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-1.2B-Thinking"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
#   attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "What is C. elegans?"

input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
).to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.1,
    top_k=50,
    top_p=0.1,
    repetition_penalty=1.05,
    max_new_tokens=512,
    streamer=streamer,
)

🔧 Fine-Tuning

We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.

Name	Description	Docs
SFT (Unsloth)	Supervised Fine-Tuning with LoRA using Unsloth.	Link
SFT (TRL)	Supervised Fine-Tuning with LoRA using TRL.	Link
DPO (TRL)	Direct Preference Optimization with LoRA using TRL.	Link

📊 Performance

Benchmarks

We compared LFM2.5-1.2B-Thinking with relevant sub-2B models on a diverse suite of benchmarks.

Model	GPQA	MMLU-Pro	IFEval	IFBench	Multi-IF	AIME25	BFCLv3
LFM2.5-1.2B-Thinking	-	-	-	-	-	-	-
LFM2.5-1.2B-Instruct	38.89	44.35	86.23	47.33	60.98	14.00	49.12
Qwen3-1.7B (Thinking)	-	-	-	-	-	-	-
Qwen3-1.7B (Instruct)	34.85	42.91	73.68	21.33	56.48	9.33	46.30
Granite 4.0-1B	24.24	33.53	79.61	21.00	43.65	3.33	52.43
Llama 3.2 1B Instruct	16.57	20.80	52.37	15.93	30.16	0.33	21.44
Gemma 3 1B IT	24.24	14.04	63.25	20.47	44.31	1.00	16.64

GPQA, MMLU-Pro, IFBench, and AIME25 follow ArtificialAnalysis's methodology. For IFEval and Multi-IF, we report the average score across strict and loose prompt and instruction accuracies. For BFCLv3, we report the final weighted average score with a custom Liquid handler to support our tool use template.

Inference speed

LFM2.5-1.2B-Thinking offers extremely fast inference speed on CPUs with a low memory profile compared to similar-sized models.

In addition, we are partnering with AMD, Qualcomm, Nexa AI, and FastFlowLM to bring the LFM2.5 family to NPUs. These optimized models are available through our partners, enabling highly efficient on-device inference.

Prefill Performance

We report prefill throughput evaluated over a range of prompt lengths.

Platform / Device	Inference	Framework	Model	1K Prefill (tok/s)	4K Prefill (tok/s)	16K Prefill (tok/s)	Memory
AMD Ryzen™ AI 395+	NPU	FastFlowLM	LFM2.5-1.2B-Thinking	1,487	2,226	1,670	1.6 GB (full context)
AMD Ryzen™ AI 9 HX 370	NPU	FastFlowLM	LFM2.5-1.2B-Thinking	1,487	2,226	1,670	1.6 GB (full context)
AMD Ryzen™ AI 7 HX 350	NPU	FastFlowLM	LFM2.5-1.2B-Thinking	1,431	2,032	1,519	1.6 GB (full context)
AMD Ryzen™ AI 5 HX 340	NPU	FastFlowLM	LFM2.5-1.2B-Thinking	1,431	2,032	1,519	1.6 GB (full context)
AMD Ryzen™ AI 9 HX 370	CPU	llama.cpp (Q4_0)	LFM2.5-1.2B-Thinking	2,975	N/A	N/A	856 MB
Qualcomm Snapdragon® X Elite	NPU	NexaML	LFM2.5-1.2B-Thinking	2,591	N/A	N/A	0.9 GB
Qualcomm Snapdragon® Gen4 (ROG Phone 9 Pro)	NPU	NexaML	LFM2.5-1.2B-Thinking	4,391	N/A	N/A	0.9 GB
Qualcomm Dragonwing IQ9 (IQ-9075, IoT)	NPU	NexaML	LFM2.5-1.2B-Thinking	2,143	N/A	N/A	0.9 GB
Qualcomm Snapdragon® Gen4 (Galaxy S25 Ultra)	CPU	llama.cpp (Q4_0)	LFM2.5-1.2B-Thinking	335	N/A	N/A	719 MB

Decode Performance

The reported results correspond to decoding 100 tokens at different context lengths.

Platform / Device	Inference	Framework	Model	Decode @1K (tok/s)	Decode @4K (tok/s)	Decode @16K (tok/s)	Memory
AMD Ryzen™ AI 395+	NPU	FastFlowLM	LFM2.5-1.2B-Thinking	60	54	49	1.6 GB (full context)
AMD Ryzen™ AI 9 HX 370	NPU	FastFlowLM	LFM2.5-1.2B-Thinking	57	54	49	1.6 GB (full context)
AMD Ryzen™ AI 7 HX 350	NPU	FastFlowLM	LFM2.5-1.2B-Thinking	63	59	52	1.6 GB (full context)
AMD Ryzen™ AI 5 HX 340	NPU	FastFlowLM	LFM2.5-1.2B-Thinking	63	59	52	1.6 GB (full context)
AMD Ryzen™ AI 9 HX 370	CPU	llama.cpp (Q4_0)	LFM2.5-1.2B-Thinking	116	N/A	N/A	856 MB
Qualcomm Snapdragon® X Elite	NPU	NexaML	LFM2.5-1.2B-Thinking	63	N/A	N/A	0.9 GB
Qualcomm Snapdragon® Gen4 (ROG Phone 9 Pro)	NPU	NexaML	LFM2.5-1.2B-Thinking	82	N/A	N/A	0.9 GB
Qualcomm Dragonwing IQ9 (IQ-9075, IoT)	NPU	NexaML	LFM2.5-1.2B-Thinking	53	N/A	N/A	0.9 GB
Qualcomm Snapdragon® Gen4 (Galaxy S25 Ultra)	CPU	llama.cpp (Q4_0)	LFM2.5-1.2B-Thinking	70	N/A	N/A	719 MB

LFM2.5-1.2B-Thinking excels at long-context inference.
On AMD NPUs with FastFlowLM, decoding throughput sustains ~46 tok/s even at the full 32K context, indicating robust long-context scalability. See detailed benchmark results (up to full context length) here.

These capabilities unlock new deployment scenarios across various devices, including vehicles, mobile devices, laptops, IoT devices, and embedded systems.

Contact

For enterprise solutions and edge deployment, contact sales@liquid.ai.

Citation

bibtex

@article{liquidai2025lfm2,
  title={LFM2 Technical Report},
  author={Liquid AI},
  journal={arXiv preprint arXiv:2511.23404},
  year={2025}
}